The　infrared　images　have　wider　practical　value　in　industry,　medicine　and　military.　They　capture　the　long　wave　at　the　infrared　camera,　which　will　cause　unique　distortions　differently　to　natural　images.　The　current　research　of　image　quality　assessment　(IQA)　for　infrared　images　has　been　largely　overlooked　so　far.　To　fill　this　void,　we　describe　a　novel　benchmark　infrared　imaging　quality　evaluation　database,　dubbed　I2QED.　The　database　includes　50　reference　(original)　images　from　other　available　infrared　image　datasets.　The　pristine　images　are　distorted　by　introducing　white　noise,　blur,　non-uniformity　and　compression　for　the　acquisition　and　transmission　of　the　long　wave　signals.　The　5000　distorted　images　with　various　human　visual　perception　quality　are　obtained　by　five　different　levels　of　degradation　for　each　type.　The　5050　images　including　reference　and　distorted　images　are　evaluated　by　30　inexperienced　observers　to　obtain　mean　opinion　scores　(MOS).　We　analyze　the　I2QED　database　using　13　traditional　and　advanced　objective　quality　evaluation　measures.　Experimental　results　confirm　the　effectiveness　and　versatility　of　the　proposed　database,　and　weaknesses　of　existing　algorithms.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.